Conventional wireless receivers may use noise and/or data covariance information in the form of a covariance matrix to suppress interference in received signal(s), e.g., multiple sample sets of a single transmitted signal, multiple different signals, or any combination thereof. Such receivers generally determine weighting factors based on the covariance matrix, and suppress interference by using the weighting factors to weight and combine the received signals. Examples of such interference suppressing receivers include chip equalizers, RAKE receivers, Generalized RAKE (GRAKE) receivers, single-input multiple-output receivers, multiple-input multiple-output receivers, etc.
Interference-suppressing receivers require accurate calculation of the covariances in the covariance matrix to accurately suppress the interference. Calculating the covariances often requires highly complex computations. The ability to accurately compute such covariances is limited by the processing power of the receiver and/or the number of received signals being weighted and combined at any given time. Thus, the complex computations associated with computing the covariance matrix often places restrictions on the operation of the wireless receiver. The wireless industry therefore continues to look for ways to improve the operation of interference suppressing receivers.